Visualizing data with ggplot2

Motivation

To build good machine learning models, you should understand your data. This is even more important if you will be presenting your model results to a non-technical (business) audience.

A good graphic or visualization can be very convincing.

ggplot is the standard for exploratory graphcis in R, and is very popular for publishing too.

Introductory Examples

library(dplyr)
library(ggplot2)
df0 <- readRDS("data/inpatient_charges_2014_clean_cardiac_50plus.RDS")

df <- df0 %>% filter(DRG.code %in% c(220:240))

ggplot(df, aes(x=reorder(factor(DRG.code), Average.Total.Payments, median, order=TRUE), y=Average.Total.Payments, fill=factor(DRG.code))) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(alpha=0.08) +
  coord_flip() +
  scale_y_continuous(breaks=scales::pretty_breaks(8)) +
  ggtitle("Average Total Payments, by DRG code") +
  labs(x="DRG code", y="Payment ($)") +
  theme(legend.position="none")

Concepts

"ggplot2 is designed to work in a layered fashion, starting with a
layer showing the raw data then adding layers of annotation and
statistical summaries."

Plot with ggplot() or quick plot with qplot()

Specify x, y and aesthetics with aes() inside ggplot(). Pass string names with aes_string().

Add layers with + such as geom’s: + geom_point()

## Let's illustrate these concepts with a simple scatterplot
ggplot(data=df, aes(x=Average.Covered.Charges, y=Average.Total.Payments)) +
    geom_point()

## easily add smoother 
ggplot(data=df, aes(x=Average.Covered.Charges, y=Average.Total.Payments)) +
  geom_point() +
  geom_smooth(method="lm")

## customize color
ggplot(data=df, aes(x=Average.Covered.Charges, y=Average.Total.Payments)) +
    geom_point(colour="red")

## customize group/color
## use pretty scales with brewer
ggplot(data=df, aes(x=Average.Covered.Charges, y=Average.Total.Payments,
                    colour=factor(DRG.code))) +
    geom_point(alpha=0.4) +
    scale_color_brewer(type="div")

## different geom: a histogram (univariate summary)
ggplot(df, aes(Average.Total.Payments)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## a bar chart
ggplot(df, aes(x=DRG.code)) + geom_bar()

Let’s explore ggplot layering with a boxplot

## distinct(df, DRG.code)

p <- ggplot(df, aes(x=factor(DRG.code), y=Average.Total.Payments))

p + geom_boxplot()

p + geom_boxplot() + geom_jitter()

p + geom_boxplot(outlier.shape = NA) + geom_jitter(alpha=0.2)

p + geom_boxplot(outlier.shape = NA) + geom_jitter(alpha=0.2) + coord_flip()

Use facets to explore data by sub-groups

df2 <- df %>% filter(Provider.State %in% c("DC", "VA", "MD"))

ggplot(df2, aes(x=factor(Provider.State), y=Average.Total.Payments)) +
    geom_jitter() +
    facet_wrap(~DRG.code) + 
    ggtitle("Payments by Provider State") +
    labs(y="Average Total Payment", x="State")

ggplot(df2, aes(x=factor(Provider.State), y=Average.Total.Payments)) +
    geom_jitter() +
    facet_grid(~DRG.code) + 
    ggtitle("Payments by Provider State") +
    labs(y="Average Total Payment", x="State")

qplot(): a quick plot, like base R plot()

## plots histogram if given 1 variable
qplot(Average.Total.Payments, data=df)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## plots scatterplot if given 2 variables
qplot(Average.Covered.Charges, Average.Total.Payments, data=df)

qplot(Average.Covered.Charges, Average.Total.Payments, data=df,
      geom="line")

Summary

ggplot is the standard for exploratory data visualizations

References:

James et al, p 45, 49

http://docs.ggplot2.org/current/

https://github.com/AmeliaMN/ggplot2_tutorial

Exercises

1. Subset data frame df2 to DC and plot Average.Medicare.Payments for each DRG.code.

2. Use a search engine to find a way to create a scatterplot matrix of the 3 Payment variables.